The DOI matches its Crossref record; 44 of the 50 references it cites were found in a registry or an index.
Distinctive Image Features from Scale-Invariant Keypoints
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Moreover, the efficiency of HF-net2 was tested and compared with several wellknown handcrafted algorithms for keypoint detection and description including Harris [4], FAST [6], SIFT [9] and ORB [8].”
From Keypoint Detection and Description through Deep Learning in Unstructured Environments (2023) · cites this paper as [9]
In the citing paper
“By including a background removal step sim-330 ilar to the WGE in [9], which makes use of a Prewitt operator in combination with a gradient filter, the authors could also demonstrate the robustness of their IP with respect to the Earth in the background.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Feature fusion was then used to synthesise the detected points into polylines which resemble parts of the spacecraft body.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Alternatively, Capuano et al. [32] introduced a new IP scheme in which three different parallel processing streams, which use the Shi-Tommasi (ST) corners detector, the HT, and the Line Segment Detector (LSD), are exploited in order to filter three sets of points and improve the robustness of the feature detection.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Furthermore, the scenarios with the Earth horizon were tackled by tuning the threshold of gradient filter to a more selective value.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Robustness proven”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc., its use in image hashing, as proposed in this paper, has proved to be every effective.”
From A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids (2019) · cites this paper as [9]
In the citing paper
“The latter, introduced by Lowe [2], have been shown to outperform the others [7].”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“This can be explained by the fact that they capture a substantial amount of information about the spatial intensity patterns, while at the same time being robust to small deformations or localisation errors.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as Lowe 2004
In the citing paper
“Traditional methods may require complex feature matching algorithms (e.g., SIFT, SURF) or additional classifiers (e.g., SVM), whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, which simplifies the process [27,28].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [27]
Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Self-citationA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Generalizing the Hough transform to detect arbitrary shapes
Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,434
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,764
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Witkin, A. (1987) · Elsevier eBooks · doi:10.1016/b978-0-08-051581-6.50036-2 · Cited by 2,375
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,030
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
Cited by
Sorted by citations
Items 48,201–48,300 of 56,203
Chen, Z., Liu, J., Lu, Z.-R., et al. (2026) · Measurement · doi:10.1016/j.measurement.2026.121393
Three-dimensional Localization of Convolutional Neural Networks Based on Domain Randomization
Zuo, G., Zhang, C., Zheng, T., Gu, Q., & Gong, D. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961846
View morphing using linear prediction of sub-space features
Mahalanobis, A., Berkowitz, P., & Shah, M. A. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.886264
Stiller, C., Bachmann, A., & Salscheider, O. (2026) · Springer eBooks · doi:10.1007/978-3-658-45276-6_18
Ensuring the Invariance of Object Images to Linear Movements for Their Recognition
Mammadov, R., Rahimova, E., & Mammadov, G. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-31956-3_12
Meng, X., Li, W., Hu, Y., & Han, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247655
TUBITAK UZAY at TRECVID 2010: Content-Based Copy Detection and Semantic Indexing.
Saracoğlu, A., Esen, E., Soysal, M., et al. (2010)
Wasserstein HOG: Local Directionality Extraction via Optimal Transport
Zhu, J.-N., Veeraraghavan, H., Jiang, J., Oh, J., Norton, L., Deasy, J., & Tannenbaum, A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3325295 · Cited by 2
Taxonomies of Cognitive Architectures
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0004
A User-Guided and Local Motion-Adaptive Framework for Virtual Product Placement in Video
Zhang, T., Seo, J., Kim, K.-M., et al. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249375
“Since it is diffi-cult for the user to select precise keypoints, we refine each user-selected point by replacing it with the nearest keypoint extracted by SIFT [9].”
Joint Segmentation of Multi-View Images by Region Correspondence
Lee, S.-C., Kwon, D., Yun, I.-D., et al. (2008) · Journal of Broadcast Engineering · doi:10.5909/jbe.2008.13.5.685
Fast Binary Descriptor Search for Keypoint Matching by Norm Ordering
Sugimura, M., Baba, T., & Tanaka, R. (2017) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2017.110
Long distance visual ground-based signaling for unmanned aerial vehicles
Grabe, V., & Nuske, S. T. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759731
Synchronising a stereoscopic surgical video stream using specular reflection
Chandelon, K., & Bartoli, A. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03232-w
“For the diffuse LRCS, we have chosen the number of SIFT matches [21], which combines reasonable invariance and fast computation.”
Object-sensitive Deep Reinforcement LearningPreprint
Li, Y., Sycara, K. P., & Iyer, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06064
Trend Analysis of Registration Technology Between Infrared Images
Kwon, H., & Kim, S. (2022) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas55662.2022.10003786
Dworzak, M., Biessikirski, A., Józef, P. Y. R. A., et al. (2015) · Przegląd Górniczy
Feature Point Detection Algorithm Based on Sequential Neural Networks and Knowledge Distillation
He, M. (2024) · International Conference on Computer Information Science and Artificial Intelligence (CISAI) · doi:10.1145/3703187.3703269
“Scale invariance is typically achieved by constructing a Gaussian pyramid for the image [12], while rotation invariance is addressed by calculating the main orientation of the feature point.”
A Game-Theoretic Approach to the Enforcement of Global Consistency in Multi-view Feature Matching
Rodolà, E., Albarelli, A., & Torsello, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14980-1_34
Pedestrian Detection Algorithm Based on Multi-Feature Cascade
Wen, J., Liu, P., Jia, C., & Wang, H. (2018) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2018.8487468
Institch: an Incremental Approach to Stitching Low-Overlap Images
Li, N., Zhao, H., & Cao, M. (2025) · IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai67447.2025.11412752
“According to the report in [28], the matching results may be desirable when τ ∈ [0 .”
A Distributed Streaming Framework for Connection Discovery Using Shared Videos
Li, X., Cheung, M., & She, J. P. M. (2017) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3120996
Automated STED microscopy for cell-biological high-throughput assays
Bierbaum, M. S. (2022) · doi:10.53846/goediss-9583
LSTM-CNN-Based Hybrid Model for Image Forgery Detection-Digital Forensics Perspective
Pawar, D., & Gowda, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3247-3_7
МЕТОД СОВМЕЩЕНИЯ ВИДЕОПОТОКОВ НА ОСНОВЕ ПЕРЕКРЫВАЮЩИХСЯ ФРАГМЕНТОВ КАДРОВ
Крыштоп, А. И. (2025) · Вектор научной мысли · doi:10.58351/2949-2041.2025.23.6.026
Mobile AR in the outdoors: watch the clouds
Simoens, P., Verbelen, T., & Dhoedt, B. (2012) · Ghent University Academic Bibliography (Ghent University)
Multimodal Attentive Feature Fusion for Object Detection
Yan, J., Wang, K., Yuan, M., et al. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242257
“Traditional object detection schemes based on the encoding of hand-crafted features (e.g., textures, colour histogram, or more complex HOG [1, 2], SIFT [3], Haar [4], ACF [5], etc.) can only generate shallow features with limited representation.”
A Three-Stage Surface-Defect Detection Pipeline for Bridge Stay-Cable Sheaths
Yujing, L. I. U., Li, J., Yi, F. A. N., et al. (2025) · Industrial Construction · doi:10.3724/j.gyjzg25062002
Object viewpoint estimation in the wild
Xiao, Y. (2021) · theses.fr (ABES) · doi:10.70675/a1b6f6ffz2eb3z4d6ezb629z5647792bde63
Li, B., Zhou, J., Zhang, H., Guo, Y., & Zheng, L. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-51498-2.00001-6
Visual Human Tracking in Wireless Cameras Networks A SURF-Based Approach
Zhou, Y., Snoussi, H., Zheng, S., et al. (2011) · IGI Global eBooks · doi:10.4018/978-1-61350-153-5.ch008
A New VLAD Method with Dense SIFT Selection Application in Image Classification
Qian, Z., Hong, Q., Huang, G., et al. (2017) · doi:10.2991/caai-17.2017.127
Sial, S. B., Ahmad, H., Farooq, O., Naveed, M., & Ayaz, Y. (2017) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3055635.3056656
Multi-Modal Ontology that Enhances Visual Content Retrieval System
Poslad, S., & Kesorn, K. (2013) · International Journal of Electrical Energy · doi:10.12720/ijoee.1.4.284-290
Feedback-based Dynamically Weighted BoF for Image Retrieval
Gao, Y., Jia, Y., Li, N., et al. (2016) · doi:10.2991/icmemtc-16.2016.296
Correspondence matching based on Higher Order Statistics for Multi-view plus Depth video sequences
Colonnese, S., Oliveira, P., Rosato, A., et al. (2014) · European Workshop on Visual Information Processing · doi:10.1109/euvip.2014.7018404
Online Sensor Hallucination via Knowledge Distillation for Multimodal Image ClassificationPreprint
Kumar, S., Banerjee, B., & Chaudhuri, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10559 · Cited by 3
Bus line trajectories classification using weightless neural networks
Barbosa, R. B. (2018) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Can humans and automatic algorithms recognize look-alike faces?
Lamba, H., Sarkar, A., Vatsa, M., et al. (2012)
Low Complexity Image Recognition Algorithms for Handheld devices
Ayyalasomayajula, P. (2013) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-5912
Object Depth Measurement Based on Feature Point Detection
Lai, Y., & Zhongsheng, W. (2019) · doi:10.2991/pntim-19.2019.66
Pushing the Limits of Real-Time Face Detection: A Comparative Analysis of YOLOv11 Models
Bokash, M., Almaktari, A., & Humbe, V. T. (2025) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta66764.2025.11132186
“Similarly, classical methods like HOG with SVM [17] , and SIFT [18] offered robustness to illumination and scale changes but were computationally expensive.”
Precision feature point tracking method using a drift-correcting template update strategy
Peng, X., Ma, Q., Zhang, Q., et al. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.805435
Automated Diagnostics of Coronary Artery Disease
Kukar, M., Kononenko, I., & Grošelj, C. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2455-9.ch053
Error-concealed Image-based Rendering
Eisemann, M. (2011) · Digitale Bibliothek Braunschweig (Verbundzentrale Göttingen (VZG)) · doi:10.24355/dbbs.084-201111011021-0
Combining Hiearachical Clustering and Naive-Bayes Nearest-Neighbor For Image Classification
Fu, C., & Jia, S. (2015) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/lemcs-15.2015.7
Prakash, R., Abraham, A., & Prasad, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28478-5_7
Large Baseline Matching of Scale Invariant Features
Delponte, E., Isgrò, F., Odone, F., et al. (2005) · Lecture notes in computer science · doi:10.1007/11553595_97
Fast landmark image retrieval based on Gaussian mixture clustering
xie, Z., & Yang, S. (2024) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1117/12.3033765
Accuracy estimation of images registration method
Wang, B., Lou, H., Qin, H., et al. (2012) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2012.6469764
A Method of Feature Matching Based on Multi-Pose Face
Xia, Y., Han, Y., Li, J., et al. (2010) · International Conference on Information Engineering and Computer Science · doi:10.1109/iciecs.2010.5678246
Particle filter based moving object tracking in dynamic scene
Haihong, C., Liu, L., & Hanlin, S. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7260251
Gao, X., Zhong, Z., Jia, Q., et al. (2026) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp69961.2026.11540659
“Early work relied on handcrafted local features, such as SIFT [6] and SURF [7], combined with descriptor aggregation schemes like Bag-of-Words (BoW) [8] and VLAD [9] to obtain compact global descriptors.”
CONTOUR-BASED METRIC AND STRUCTURAL DESCRIPTORS FOR ROBUST PLANAR SHAPE RECOGNITION IN BINARY IMAGES
Miratoev, Z. (2025) · World Scientific Research Journal · doi:10.64941/xh4gwx53
Liang, Y., Wang, T., Liang, Y., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3635111
Mei, T., & Rui, Y. (2018) · Encyclopedia of Database Systems · doi:10.1007/978-1-4614-8265-9_1014
Local feature method robust to compression noise using MSER and magnitudes of Zernike moments
Lee, J., Hwang, S.-K., & Kim, W.-Y. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5582994
Combining Filter Bank and KSH for Image Retrieval in Bone Scintigraphy
Xu, H., Qiao, Y., Gu, Y., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63830-6_17
Automatic feature selection for Landsat-8 and Sentinel-2 image co-registration using SURFFANN
Ambati, R., Reddy, C., Patel, S., et al. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628687
Towards Full Deep Learning-based SLAM
Khoyani, A. (2023) · doi:10.22215/etd/2023-15731
Xin, Y., Li, W., Xu, X. W., et al. (2024) · Micromachines · doi:10.3390/mi15070834
“The most widely used feature description methods are the Harris corner algorithm [41] and the Scale-Invariant Feature Transform (SIFT) algorithm [42,43].”
MethodNagano, H., Wu, X., Hiramatsu, K., et al. (2017) · TRECVID
Pengenalan Motif Karawo Menggunakan Ekstraksi Fitur SIFT Dan Approximate Nearest NeighborPreprint
Syahrial, S. (2019) · doi:10.31227/osf.io/zb857
Dimensional Object Recognition using SVM -KNN augmented by local and global feature
Chandrasekar, C. (2012)
ViewSynth: Learning Local Features from Depth using View Synthesis
Mahmud, J., Singh, R., Akiva, P., et al. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.2
GPU Accelerated Parallel Iris Localization
Sinha, A. (2013)
Um Sistema de Autenticação por Faces Usando Filtros de Correlação em Vídeos
de Oliveira, J. F. L., da Silva, E. A. B., Cardoso, M. A. P., et al. (2009) · Anais do XXVII Simpósio Brasileiro de Telecomunicações · doi:10.14209/sbrt.2009.55705
A Novel Image Signature based on Local Representative Pattern Mining
Wang, G., Gao, K., & Li, J. (2014) · doi:10.1145/2632856.2632924
Schairer, T., Herholz, S., Huhle, B., et al. (2010) · 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video · doi:10.1109/3dtv.2010.5506311
Head Detection by the Adaboost Algorithm for Head Pose Estimation
Liu, X. Y., Zhu, S. S., Wu, S. Q., et al. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.427-429.1696
Object Counting Based on Its Panoramic Image Representation
Chu, T.-C., & Huang, F. (2019) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw46550.2019.8991858
Fusing Joint Multiplexed Curvature Gradient and Color Features for Image Retrieval
Zhang, G., & Zhang, H. (2026) · IEEE Access · doi:10.1109/access.2026.3715527
Traffic sign 3D recognition: A monocular approach based on keypoint matching
Abdeldaim, A., Araar, O., & Irki, Z. (2025) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070251353818
“32 For the descriptions, five algorithms were considered: SIFT 29 ; SURF 28 ; ORB 30 ; KAZE 31 and BRISK.”
Guo, J., Zhao, L., Liang, Q., Du, X., Xu, Y., & Li, X. (2026) · Applied Sciences · doi:10.3390/app16063018
Parallel discrete geodesic algorithms
Du, J. (2020) · doi:10.32657/10356/143580
Xu, Z., Saputro, A. H., Ibrahim, A. A., & Zulkifley, M. A. (2026) · IEEE Access · doi:10.1109/access.2026.3731988
Depth Mapping Using the Hierarchical Reconstruction of Multiple Sequence
Wang, L., & Yang, L. (2013) · doi:10.1109/msn.2013.84
A framework for associated news story retrieval
Younessian, E. (2013) · doi:10.32657/10356/54903
A Vehicle Type Recognition Network Based on Feature Comparison and Mixture of Experts Model
Hu, T., Zhao, X., & Yang, L. (2026) · Vehicles · doi:10.3390/vehicles8050101
“For instance, feature descriptors such as SIFT [4] and HOG [5] are widely utilized.”
Localisation par l'image en milieu urbain : application à la réalité augmentée
Fond, A. (2018) · theses.fr (ABES) · doi:10.70675/ecbf8e26zc6bcz4a54zbba7z5fef9ba17cd1
Saliency based natural image understanding
Li, Q., Zhou, Y., & Xu, L. (2011) · doi:10.1109/acpr.2011.6166648
Real-time detection of traffic events using smart cameras
Macesic, M., Jelača, V., Niño-Castañeda, J. O., et al. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.909461
Rojas, O. A. P., Sanchez-Torres, G., & Branch-Bedoya, J. W. (2026) · Computers · doi:10.3390/computers15090553
Multi-context Deep Convolutional Features and Exemplar-SVMs for Scene Parsing
Cui, X., Qu, H., Wang, S., et al. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7299-4_41
Methods for Reliable Robot Vision with a Dioptric System
Martínez-Martín, E., & del, A. P. (2010) · InTech eBooks · doi:10.5772/9306
Multi-robot target verification with reachability constraints
Grady, D. K., Moll, M., Hegde, C., et al. (2012) · IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) · doi:10.1109/ssrr.2012.6523873
Social Robot Path Planning based on a Global Perspective Using Optical Flow
Mendoza, E. G. P., Patiño, R., & Cardinale, Y. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02323-3
Ismael, B., Seshathiri, S., & Esakki, B. (2017) · International Conference on Mechatronics and Robotics Engineering (ICMRE) · doi:10.1145/3068796.3068824
Edgel templates for fast planar object detection and pose estimation
Lee, T., & Soatto, S. (2011) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2011.6143486
3GeM Pooling: Direction-aware and Compact Global Descriptors for Visual Place Recognition
Zhang, X., Lu, F., Ye, C., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461747
Graph dynamics : learning and representation
Ribeiro, A. F. (2006) · DSpace@MIT (Massachusetts Institute of Technology)
Automatic Picture-Matching of Crested Newts
Magnette, G., Didry, Y., & Mestdagh, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88207-5_32
Enhancing the Effectiveness of Local Descriptor Based Image Matching
Hossain, M. T., Teng, S. W., Zhang, D., et al. (2018) · Digital Image Computing: Techniques and Applications · doi:10.1109/dicta.2018.8615800
Can Boosting with SVM as Week Learners Help?Preprint
Govindaraj, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.05242
Texture Recognition Methodology Using Convolutional Autoencoders for MLA Images of Rock ParticlesPreprint
Roslin, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7355986
Safavigerdini, K., Yaghooti, B., Abadi, A. E. Z. S., et al. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00077
“Fundamental techniques such as feature extraction [14, 17] and dimensionality reduction [13, 26] form the backbone of these diverse tasks.”
HyperStitch: A high-quality stitching method for unmanned aerial vehicle hyperspectral images
Xie, T., Yu, Z., Ji, X., et al. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111962
Metadata
- Authors
- Lowe, D. G.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Lowe, David G.
- ORCID
- Lowe, David G.
- Venue
- International Journal of Computer Vision
- Published
- 2004-11
- Volume
- 60
- Issue
- 2
- Pages
- 91-110
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2151103935
- Semantic Scholar
- 8c04f169203f9e55056a6f7f956695babe622a38
- Cited by
- 55,589
- References
- 50
- Citations by year
- 2026: 1480 · 2025: 2400 · 2024: 2519 · 2023: 2604 · 2022: 2705 · 2021: 3066 · 2020: 2933 · 2019: 3240 · 2018: 3232 · 2017: 3363 · 2016: 3431 · 2015: 3747 · 2014: 3569 · 2013: 3333 · 2012: 3003
- FWCI
- 699.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- feature matching, affine invariance, rotation invariance, object recognition, nearest neighbor matching, Hough transform, pose estimation, occlusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
